The removal of sour gas components from the gas streams using chemical solvents, such as MDEA, is a requirement in most hydrocarbon processing plants. The acid gas constituents (H2S and CO2) react with an aqueous solution in a high-pressure absorber. Subsequently, the solvent is stripped from the acid gas in the regenerator at elevated temperature to reuse it. Figure 1 illustrates a typical gas treating plant employing an alkanolamine.
One of the most frequent faults in gas sweetening is amine f...

The removal of sour gas components from the gas streams using chemical solvents, such as MDEA, is a requirement in most hydrocarbon processing plants. The acid gas constituents (H2S and CO2) react with an aqueous solution in a high-pressure absorber. Subsequently, the solvent is stripped from the acid gas in the regenerator at elevated temperature to reuse it. Figure 1 illustrates a typical gas treating plant employing an alkanolamine.
One of the most frequent faults in gas sweetening is amine foaming, which results in the loss of proper vapor-liquid contact, solution hold up and poor solution distribution. Some root causes of foaming includes accumulation of heavy hydrocarbon and solid particle in amine and antifoam trouble. The consequence is off-spec product, downtime and loss of amine and energy. Whenever operators distinguish foaming based process measurement trends, a short term measure is manual injecting of antifoam agent. However, disadvantage of this approach is misdetection due to existence of numerous variables, low experience and unconscious operators. Also, there are some other process disturbances in the system originated in downstream or upstream that can mislead the operator to detect foaming. On the other hand, overdose injection of antifoam has adverse effects on the filtration system.
This work proposes a decision support software based on principal component analysis for detection of foaming in a sweetening plant. From the many process variables, operators tend to make decisions based on only one or two of them. Consequently, important information included in other variables, or in their relations, is discarded. This work proposes the use of PCA [1] to reduce the monitoring space without discarding relevant process variance. Due to the importance of process disturbances, PCA cannot be applied using their standard statistics (T2 and SPE), due to the
high number of false alarms they produce. However, a 2D scatter plot in the reduced space (Fig. 2) allows performing an early and reliable identification of foaming, thereby supporting operator decisions and reducing operating costs. Results show fast and efficient data analysis for practical fault detection and qualitative decision-making support. Further work is underway regarding quantitative assessment of confidence levels.